Workpiece sensing for process management and orchestration

ABSTRACT

Systems and methods for identifying a workpiece in a processing environment may utilize one or more sensors for digitally recording visual information and providing that information to an industrial workflow. The sensor(s) may be positioned to record at least one image of the workpiece at a location where a specified position and orientation thereof is required. A processor may determine, from the recorded image(s) and a stored digital model, whether the workpiece conforms to the specified position and orientation.

CROSS-REFERENCE TO RELATED APPLICATION

This is a continuation-in-part of, and incorporates herein by referencein its entirety, U.S. Ser. No. 15/889,524, filed on Feb. 6, 2018.

FIELD OF THE INVENTION

The field of the invention relates, generally, to monitoring ofindustrial environments that involve processing of “workpieces” from aninitial state to a more (or fully) finished state, and in particular tosystems and methods for recognizing the presence, orientation andalignment of workpieces in the course of manufacturing processes.

BACKGROUND

In modern manufacturing processes, workpieces may proceed through aseries of process steps that progressively transform the workpiece fromraw material into a finished product. At each step (which may occur at aseparate station), it is important to determine when the next workpiecearrives and when it is properly seated—e.g., in a fixture or at adesired position on a conveyor line—for processing. This ensures properexecution of the current stage of manufacture as well as downstream“process orchestration.” Multiple proximity sensors are typically usedto sense workpiece presence and orientation.

A proximity sensor detects the presence of nearby objects without anyphysical contact. Current sensing approaches in the industry typicallyeither emit an electromagnetic field or a beam of electromagneticradiation and sense changes in the field or return signal caused by theworkpiece. The proximity-sensing modality is selected based on thematerial properties of the components being sensed. Inductive proximitysensors are used for non-contact detection of metallic objects. Anoscillator within the sensor generates an electromagnetic field, andproximity is sensed based on perturbations of the electromagnetic fieldcaused by the presence of a metallic part within the sensing range.Photoelectric sensors, also known as photo-eyes, emit beams of infraredor visible-spectrum light and utilize reflectors to monitor beam-breakor sense diffuse reflection from workpieces passing in front of thesensor.

Multiple proximity sensors are typically used to sense if a workpiece isproperly nestled in a workholding fixture or located at a desiredposition on a conveyor line. Higher-level controllers such asprogrammable logic controllers (PLCs) or manufacturing execution systems(MESs) then perform downstream process orchestration based on the outputof the proximity sensor network. For example, in an automotivebody-in-white spot-welding process, human operators feed sheet-metalcomponents into “operator load stations.” A human operator steps intothe feeding station, sets a piece of sheet metal into a workholdingfixture, and then steps back outside the zone. Proximity sensorsbordering on the periphery of the workholding fixture are triggered whenthe sheet-metal component is brought within their sensing range. Whenthe human has achieved correct alignment of the workpiece within thefixture, all proximity sensors will have been triggered and a logicalcondition within a PLC is also triggered. A downstream robotic handlingtask is then signalled by the PLC, and a robot is dispatched to pick theworkpiece from a pre-programmed location to carry out the weldingprocess.

Though in widespread use, proximity sensors have limitations. Inductiveand optical sensors are typically one-dimensional devices that are proneto fouling and misalignment due to their close proximity to amanufacturing process. A proximity sensor can only sense distance, andas a consequence, their information resolution is limited; many sensorsmay be needed to unambiguously ascertain the precise position of acomplex workpiece (or a simple workpiece in a complex environment).Accurate sensing may be further complicated by the presence of frames orother bearing fixtures that may partially surround the workpiece andmove during its processing. What is needed, therefore, is an approachtoward object sensing that can judge object position and orientationbased on relatively limited sensed information.

SUMMARY

Embodiments of the present invention determine the position andorientation of a workpiece as it approaches and is handled at aprocessing station. The invention facilitates the control ofmanufacturing process-related equipment to achieve desired metricsrelated to throughput, quality, and overall equipment effectiveness(OEE).

Embodiments of the present invention may be deployed in safetyapplications for the purposes of mitigation of hazardous situations thatmay be caused by a machine or robotic system. When used in safetyapplications, outputs may conform to the requirements outlined in ISO13849-1:2015 Safety of machinery—Safety-related parts of controlsystems—Part 1: General principles for design. Embodiments of thepresent invention may also be deployed in non-safety applications, wherethere are no claims made to the reliability of the classificationalgorithms. In these non-safety applications, failures related to theidentification process do not lead to unsafe hazardous situationsbecause other safeguarding measures are already in place—for examplephysical fencing around a machine. The present invention may be used forprocess orchestration, data collection, dimensional metrology or othernon-safety applications that benefit from non-contact 3D workpiecedetermination.

Accordingly, in a first aspect, the invention pertains to a system foridentifying a workpiece in a processing environment. In variousembodiments, the system comprises at least one sensor for digitallyrecording visual information, at least one sensor being positioned torecord at least one image of the workpiece at a location where aspecified position and orientation thereof is required; a computermemory for storing a digital model of the workpiece; and a processorconfigured to determine, from the recorded image(s) and the storeddigital model, whether the workpiece conforms to the specified positionand orientation.

The digital model may include a general representation of the workpieceand a specific representation of the workpiece in the specified positionand orientation. The processor may be configured to computationallygenerate, from at least one recorded image, a 3D spatial representationof the workpiece. The processor may be further configured to generate a3D voxel-grid volumetric representation of the location.

In various embodiments, the computer memory stores digital models of aplurality of workpieces; the processor may be further configured torecognize a workpiece based on the recorded image(s) and comparisonthereof to the stored digital models. The digital models include offsetboundaries to reflect workpiece variation and/or tolerances in workpieceposition and/or orientation. In some embodiments, the digital model is aspatial representation of the workpiece, e.g., as a mesh generated froma point cloud or CAD representation, whereas in other embodiments, thedigital model is a machine-learning representation such as a trainedconvolutional neural network (CNN). The digital model may includerepresentations of multiple discrete, sequential states.

In another aspect, the invention relates to a method for identifying aworkpiece in a processing environment. In various embodiments, themethod comprises the steps of digitally recording at least one image ofthe workpiece where a specified position and orientation thereof isrequired; storing a digital model of the workpiece; computationallydetermining, from the at least one recorded image and the stored digitalmodel, whether the workpiece conforms to the specified position andorientation, and only if so, processing the workpiece.

In various embodiments, the digital model includes a generalrepresentation of the workpiece and a specific representation of theworkpiece in the specified position and orientation. The method mayfurther comprise the step of computationally generating, from at leastone recorded image, a 3D spatial representation of the workpiece, andmay still further comprise generating a 3D voxel-grid volumetricrepresentation of the location.

In some embodiments, digital models of a plurality of workpieces arestored, and the method also includes the step of computationallyrecognizing a workpiece based on the recorded image(s) and comparisonthereof to the stored digital models. Each of the digital models mayinclude offset boundaries, e.g., to account for workpiece variations. Insome embodiments, the digital model is a CAD representation, whereas inother embodiments, the digital model is a machine-learningrepresentation. The digital model may include representations ofmultiple discrete, sequential states.

The digital model of the workpiece may be created according to stepscomprising removing all non-workpiece objects from the processingenvironment; digitally recording an image of the processing environmentas at least one background image; physically moving a workpiece into theprocessing environment; digitally recording an image of the workpiece inthe specified position and orientation; subtracting the background imagefrom the image of the workpiece in the specified position andorientation to produce a difference image; and storing the differenceimage as the digital model of the workpiece.

In general, as used herein, the term “substantially” means±10%, and insome embodiments, ±5%. In addition, reference throughout thisspecification to “one example,” “an example,” “one embodiment,” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the example is included inat least one example of the present technology. Thus, the occurrences ofthe phrases “in one example,” “in an example,” “one embodiment,” or “anembodiment” in various places throughout this specification are notnecessarily all referring to the same example. Furthermore, theparticular features, structures, routines, steps, or characteristics maybe combined in any suitable manner in one or more examples of thetechnology. The headings provided herein are for convenience only andare not intended to limit or interpret the scope or meaning of theclaimed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the sameparts throughout the different views. Also, the drawings are notnecessarily to scale, with an emphasis instead generally being placedupon illustrating the principles of the invention. In the followingdescription, various embodiments of the present invention are describedwith reference to the following drawings, in which:

FIG. 1 is a perspective view of a monitored workspace in accordance withan embodiment of the invention.

FIG. 2 schematically illustrates classification of regions within themonitored workspace in accordance with an embodiment of the invention.

FIG. 3 schematically illustrates a control system in accordance with anembodiment of the invention.

FIGS. 4A-4E schematically illustrate object-monitoring systems inaccordance with various embodiments of the invention.

FIG. 5 schematically illustrates the definition of progressive safetyenvelopes in proximity to a piece of industrial machinery.

DETAILED DESCRIPTION

In the following discussion, we describe an integrated system formonitoring a workspace, classifying regions therein, dynamicallyidentifying safe states, and identifying and tracking workpieces. Insome cases the latter function involves semantic analysis of a robot inthe workspace and identification of the workpieces with which itinteracts. It should be understood, however, that these various elementsmay be implemented separately or together in desired combinations; theinventive aspects discussed herein do not require all of the describedelements, which are set forth together merely for ease of presentationand to illustrate their interoperability. The system as describedrepresents merely one embodiment.

1. Workspace Monitoring

Refer first to FIG. 1, which illustrates a representative 3D workspace100 monitored by a plurality of sensors representatively indicated at102 ₁, 102 ₂, 102 ₃. The sensors 102 may be conventional optical sensorssuch as cameras, e.g., 3D time-of-flight cameras, stereo vision cameras,or 3D LIDAR sensors or radar-based sensors, ideally with high framerates (e.g., between 25 FPS and 100 FPS). The mode of operation of thesensors 102 is not critical so long as a 3D representation of theworkspace 100 is obtainable from images or other data obtained by thesensors 102. As shown in the figure, sensors 102 collectively cover andcan monitor the workspace 100, which includes a robot 106 controlled bya conventional robot controller 108. The robot interacts with variousworkpieces W, and a person P in the workspace 100 may interact with theworkpieces and the robot 108. The workspace 100 may also contain variousitems of auxiliary equipment 110, which can complicate analysis of theworkspace by occluding various portions thereof from the sensors.Indeed, any realistic arrangement of sensors will frequently be unableto “see” at least some portion of an active workspace. This isillustrated in the simplified arrangement of FIG. 1: due to the presenceof the person P, at least some portion of robot controller 108 may beoccluded from all sensors. In an environment that people traverse andwhere even stationary objects may be moved from time to time, theunobservable regions will shift and vary.

As shown in FIG. 2, embodiments of the present invention classifyworkspace regions as occupied, unoccupied (or empty), or unknown. Forease of illustration, FIG. 2 shows two sensors 202 ₁, 202 ₂ and theirzones of coverage 205 1 , 205 ₂ within the workspace 200 in twodimensions; similarly, only the 2D footprint 210 of a 3D object isshown. The portions of the coverage zones 205 between the objectboundary and the sensors 200 are marked as unoccupied, because eachsensor affirmatively detects no obstructions in this intervening space.The space at the object boundary is marked as occupied. In a coveragezone 205 beyond an object boundary, all space is marked as unknown; thecorresponding sensor is configured to sense occupancy in this regionbut, because of the intervening object 210, cannot do so.

With renewed reference to FIG. 1, data from each sensor 102 is receivedby a control system 112. The volume of space covered by eachsensor—typically a solid cone—may be represented in any suitablefashion, e.g., the space may be divided into a 3D grid of small (5 cm,for example) cubes or “voxels” or other suitable form of volumetricrepresentation. For example, workspace 100 may be represented using 2Dor 3D ray tracing, where the intersections of the 2D or 3D raysemanating from the sensors 102 are used as the volume coordinates of theworkspace 100. This ray tracing can be performed dynamically or via theuse of precomputed volumes, where objects in the workspace 100 arepreviously identified and captured by control system 112. Forconvenience of presentation, the ensuing discussion assumes a voxelrepresentation; control system 112 maintains an internal representationof the workspace 100 at the voxel level, with voxels marked as occupied,unoccupied, or unknown.

FIG. 3 illustrates, in greater detail, a representative embodiment ofcontrol system 112, which may be implemented on a general-purposecomputer. The control system 112 includes a central processing unit(CPU) 305, system memory 310, and one or more non-volatile mass storagedevices (such as one or more hard disks and/or optical storage units)312. The system 112 further includes a bidirectional system bus 315 overwhich the CPU 305, memory 310, and storage device 312 communicate witheach other as well as with internal or external input/output (I/O)devices such as a display 320 and peripherals 322, which may includetraditional input devices such as a keyboard or a mouse). The controlsystem 112 also includes a wireless transceiver 325 and one or more I/Oports 327. Transceiver 325 and I/O ports 327 may provide a networkinterface. The term “network” is herein used broadly to connote wired orwireless networks of computers or telecommunications devices (such aswired or wireless telephones, tablets, etc.). For example, a computernetwork may be a local area network (LAN) or a wide area network (WAN).When used in a LAN networking environment, computers may be connected tothe LAN through a network interface or adapter; for example, asupervisor may establish communication with control system 112 using atablet that wirelessly joins the network. When used in a WAN networkingenvironment, computers typically include a modem or other communicationmechanism. Modems may be internal or external, and may be connected tothe system bus via the user-input interface, or other appropriatemechanism. Networked computers may be connected over the Internet, anIntranet, Extranet, Ethernet, or any other system that providescommunications. Some suitable communications protocols include TCP/IP,UDP, or OSI, for example. For wireless communications, communicationsprotocols may include IEEE 802.11x (“Wi-Fi”), BLUETOOTH, ZigBee, IrDa,near-field communication (NFC), or other suitable protocol. Furthermore,components of the system may communicate through a combination of wiredor wireless paths, and communication may involve both computer andtelecommunications networks.

CPU 305 is typically a microprocessor, but in various embodiments may bea microcontroller, peripheral integrated circuit element, a CSIC(customer-specific integrated circuit), an ASIC (application-specificintegrated circuit), a logic circuit, a digital signal processor, aprogrammable logic device such as an FPGA (field-programmable gatearray), PLD (programmable logic device), PLA (programmable logic array),RFID processor, graphics processing unit (GPU), smart chip, or any otherdevice or arrangement of devices that is capable of implementing thesteps of the processes of the invention.

The system memory 310 contains a series of frame buffers 335, i.e.,partitions that store, in digital form (e.g., as pixels or voxels, or asdepth maps), images obtained by the sensors 102; the data may actuallyarrive via I/O ports 327 and/or transceiver 325 as discussed above.System memory 310 contains instructions, conceptually illustrated as agroup of modules, that control the operation of CPU 305 and itsinteraction with the other hardware components. An operating system 340(e.g., Windows or Linux) directs the execution of low-level, basicsystem functions such as memory allocation, file management andoperation of mass storage device 312. At a higher level, and asdescribed in greater detail below, an analysis module 342 registers theimages in frame buffers 335 and analyzes them to classify regions of themonitored workspace 100. The result of the classification may be storedin a space map 345, which contains a volumetric representation of theworkspace 100 with each voxel (or other unit of representation) labeled,within the space map, as described herein. Alternatively, space map 345may simply be a 3D array of voxels, with voxel labels being stored in aseparate database (in memory 310 or in mass storage 312).

Control system 112 may also control the operation or machinery in theworkspace 100 using conventional control routines collectively indicatedat 350. As explained below, the configuration of the workspace and,consequently, the classifications associated with its voxelrepresentation may well change over time as persons and/or machines moveabout, and control routines 350 may be responsive to these changes inoperating machinery to achieve high levels of safety. All of the modulesin system memory 310 may be programmed in any suitable programminglanguage, including, without limitation, high-level languages such as C,C++, C#, Ada, Basic, Cobra, Fortran, Java, Lisp, Perl, Python, Ruby, orlow-level assembly languages.

1.1 Sensor Registration

In a typical multi-sensor system, the precise location of each sensor102 with respect to all other sensors is established during setup.Sensor registration is usually performed automatically, and should be assimple as possible to allow for ease of setup and reconfiguration.Assuming for simplicity that each frame buffer 335 stores an image(which may be refreshed periodically) from a particular sensor 102,analysis module 342 may register sensors 102 by comparing all or part ofthe image from each sensor to the images from other sensors in framebuffers 335, and using conventional computer-vision techniques toidentify correspondences in those images. Suitable global-registrationalgorithms, which do not require an initial registration approximation,generally fall into two categories: feature-based methods andintensity-based methods. Feature-based methods identify correspondencesbetween image features such as edges while intensity-based methods usecorrelation metrics between intensity patterns. Once an approximateregistration is identified, an Iterative Closest Point (ICP) algorithmor suitable variant thereof may be used to fine-tune the registration.

If there is sufficient overlap between the fields of view of the varioussensors 102, and sufficient detail in the workspace 100 to providedistinct sensor images, it may be sufficient to compare images of thestatic workspace. If this is not the case, a “registration object”having a distinctive signature in 3D can be placed in a location withinworkspace 100 where it can be seen by all sensors. Alternatively,registration can be achieved by having the sensors 102 record images ofone or more people standing in the workspace or walking throughout theworkspace over a period of time, combining a sufficient number ofpartially matching images until accurate registration is achieved.

Registration to machinery within the workspace 100 can, in some cases,be achieved without any additional instrumentation, especially if themachinery has a distinctive 3D shape (for example, a robot arm), so longas the machinery is visible to at least one sensor registered withrespect to the others. Alternatively, a registration object can be used,or a user interface, shown in display 320 and displaying the sceneobserved by the sensors, may allow a user to designate certain parts ofthe image as key elements of the machinery under control. In someembodiments, the interface provides an interactive 3D display that showsthe coverage of all sensors to aid in configuration. If the system is beconfigured with some degree of high-level information about themachinery being controlled (for purposes of control routines 350, forexample)—such as the location(s) of dangerous part or parts of themachinery and the stopping time and/or distance—analysis module 342 maybe configured to provide intelligent feedback as to whether the sensorsare providing sufficient coverage, and suggest placement for additionalsensors.

For example, analysis module 342 can be programmed to determine theminimum distance from the observed machinery at which it must detect aperson in order to stop the machinery by the time the person reaches it(or a safety zone around it), given conservative estimates of walkingspeed. (Alternatively, the required detection distance can be inputdirectly into the system via display 320.) Optionally, analysis module342 can then analyze the fields of view of all sensors to determinewhether the space is sufficiently covered to detect all approaches. Ifthe sensor coverage is insufficient, analysis module 342 can propose newlocations for existing sensors, or locations for additional sensors,that would remedy the deficiency. Otherwise, the control system willdefault to a safe state and control routines 350 will not permitmachinery to operate unless analysis module 342 verifies that allapproaches can be monitored effectively. Use of machine learning andgenetic or evolutionary algorithms can be used to determine optimalsensor placement within a cell. Parameters to optimize include but arenot limited to minimizing occlusions around the robot during operationand observability of the robot and workpieces.

If desired, this static analysis may include “background” subtraction.During an initial startup period, when it may be safely assumed thereare no objects intruding into the workspace 100, analysis module 342identifies all voxels occupied by the static elements. Those elementscan then be subtracted from future measurements and not considered aspotential intruding objects. Nonetheless, continuous monitoring isperformed to ensure that the observed background image is consistentwith the space map 345 stored during the startup period. Background canalso be updated if stationary objects are removed or are added to theworkspace

There may be some areas that sensors 102 cannot observe sufficiently toprovide safety, but that are guarded by other methods such as cages,etc. In this case, the user interface can allow the user to designatethese areas as safe, overriding the sensor-based safety analysis.Safety-rated soft-axis and rate limitations can also be used to limitthe envelope of the robot to improve performance of the system.

Once registration has been achieved, sensors 102 should remain in thesame location and orientation while the workspace 100 is monitored. Ifone or more sensors 102 are accidentally moved, the resulting controloutputs will be invalid and could result in a safety hazard. Analysismodule 342 may extend the algorithms used for initial registration tomonitor continued accuracy of registration. For example, during initialregistration analysis module 342 may compute a metric capturing theaccuracy of fit of the observed data to a model of the work cell staticelements that is captured during the registration process. As the systemoperates, the same metric can be recalculated. If at any time thatmetric exceeds a specified threshold, the registration is considered tobe invalid and an error condition is triggered; in response, if anymachinery is operating, a control routine 350 may halt it or transitionthe machinery to a safe state.

1.2 Identifying Occupied and Potentially Occupied Areas

Once the sensors have been registered, control system 112 periodicallyupdates space map 345—at a high fixed frequency (e.g., every analysiscycle) in order to be able to identify all intrusions into workspace100. Space map 345 reflects a fusion of data from some or all of thesensors 102. But given the nature of 3D data, depending on the locationsof the sensors 102 and the configuration of workspace 100, it ispossible that an object in one location will occlude the sensor's viewof objects in other locations, including objects (which may includepeople or parts of people, e.g. arms) that are closer to the dangerousmachinery than the occluding object. Therefore, to provide a reliablysafe system, the system monitors occluded space as well as occupiedspace.

In one embodiment, space map 345 is a voxel grid. In general, each voxelmay be marked as occupied, unoccupied or unknown; only empty space canultimately be considered safe, and only when any additional safetycriteria—e.g., minimum distance from a piece of controlled machinery—issatisfied. Raw data from each sensor is analyzed to determine whether,for each voxel, an object or boundary of the 3D mapped space has beendefinitively detected in the volume corresponding to that voxel. Toenhance safety, analysis module 342 may designate as empty only voxelsthat are observed to be empty by more than one sensor 102. Again, allspace that cannot be confirmed as empty is marked as unknown. Thus, onlyspace between a sensor 102 and a detected object or mapped 3D spaceboundary along a ray may be marked as empty.

If a sensor detects anything in a given voxel, all voxels that lie onthe ray beginning at the focal point of that sensor and passing throughthe occupied voxel, and which are between the focal point and theoccupied voxel, are classified as unoccupied, while all voxels that liebeyond the occupied voxel on that ray are classified as occluded forthat sensor; all such occluded voxels are considered “unknown.”Information from two or more sensors may be combined to determine whichareas are occluded from the sensors; these areas are considered unknownand therefore unsafe. Analysis module 342 may finally mark as“unoccupied” only voxels or workspace volumes that have beenpreliminarily marked at least once (or, in some embodiments, at leasttwice) as “unoccupied.” Based on the markings associated with the voxelsor discrete volumes within the workspace, analysis module 342 may mapone or more safe volumetric zones within space map 345. These safe zonesare outside a safety zone of the machinery and include only voxels orworkspace volumes marked as unoccupied.

A common failure mode of active optical sensors that depend onreflection, such as LIDAR and time-of-flight cameras, is that they donot return any signal from surfaces that are insufficiently reflective,and/or when the angle of incidence between the sensor and the surface istoo shallow. This can lead to a dangerous failure because this signalcan be indistinguishable from the result that is returned if no obstacleis encountered; the sensor, in other words, will report an empty voxeldespite the possible presence of an obstacle. This is why ISO standardsfor e.g. 2D LIDAR sensors have specifications for the minimumreflectivity of objects that must be detected; however, thesereflectivity standards can be difficult to meet for some 3D sensormodalities such as time-of-flight. In order to mitigate this failuremode, analysis module 342 marks space as empty only if some obstacle isdefinitively detected at further range along the same ray. By pointingsensors slightly downward so that most of the rays will encounter thefloor if no obstacles are present, it is possible to conclusivelyanalyze most of the workspace 100. But if the sensed light level in agiven voxel is insufficient to definitively establish emptiness or thepresence of a boundary, the voxel is marked as unknown. The signal andthreshold value may depend on the type of sensor being used. In the caseof an intensity-based 3D sensor (for example, a time-of-flight camera)the threshold value can be a signal intensity, which may be attenuatedby objects in the workspace of low reflectivity. In the case of a stereovision system, the threshold may be the ability to resolve individualobjects in the field of view. Other signal and threshold valuecombinations can be utilized depending on the type of sensor used.

A safe system can be created by treating all unknown space as though itwere occupied. However, in some cases this may be overly conservativeand result in poor performance. It is therefore desirable to furtherclassify unknown space according to whether it could potentially beoccupied. As a person moves within a 3D space, he or she will typicallyocclude some areas from some sensors, resulting in areas of space thatare temporarily unknown (see FIG. 1). Additionally, moving machinerysuch as an industrial robot arm can also temporarily occlude some areas.When the person or machinery moves to a different location, one or moresensors will once again be able to observe the unknown space and returnit to the confirmed-empty state in which it is safe for the robot ormachine to operate. Accordingly, in some embodiments, space may also beclassified as “potentially occupied.” Unknown space is consideredpotentially occupied when a condition arises where unknown space couldbe occupied. This could occur when unknown space is adjacent to entrypoints to the workspace or if unknown space is adjacent to occupied orpotentially occupied space. The potentially occupied space “infects”unknown space at a rate that is representative of a human moving throughthe workspace. Potentially occupied space stays potentially occupieduntil it is observed to be empty. For safety purposes, potentiallyoccupied space is treated the same as occupied space. It may bedesirable to use probabilistic techniques such as those based onBayesian filtering to determine the state of each voxel, allowing thesystem to combine data from multiple samples to provide higher levels ofconfidence in the results. Suitable models of human movement, includingpredicted speeds (e.g., an arm may be raised faster than a person canwalk), are readily available.

2. Classifying Objects

For many applications, the classification of regions in a workspace asdescribed above may be sufficient—e.g., if control system 112 ismonitoring space in which there should be no objects at all duringnormal operation. In many cases, however, it is desirable to monitor anarea in which there are at least some objects during normal operation,such as one or more machines and workpieces on which the machine isoperating. In these cases, analysis module 342 may be configured toidentify intruding objects that are unexpected or that may be humans.One suitable approach to such classification is to cluster individualoccupied voxels into objects that can be analyzed at a higher level.

To achieve this, analysis module 342 may implement any of severalconventional, well-known clustering techniques such as Euclideanclustering, K-means clustering and Gibbs-sampling clustering. Any ofthese or similar algorithms can be used to identify clusters of occupiedvoxels from 3D point cloud data. Mesh techniques, which determine a meshthat best fits the point-cloud data and then use the mesh shape todetermine optimal clustering, may also be used. Once identified, theseclusters can be useful in various ways.

One simple way clustering can be used is to eliminate small groups ofoccupied or potentially occupied voxels that are too small to possiblycontain a person. Such small clusters may arise from occupation andocclusion analysis, as described above, and can otherwise cause controlsystem 112 to incorrectly identify a hazard. Clusters can be trackedover time by simply associating identified clusters in each image framewith nearby clusters in previous frames or using more sophisticatedimage-processing techniques. The shape, size, or other features of acluster can be identified and tracked from one frame to the next. Suchfeatures can be used to confirm associations between clusters from frameto frame, or to identify the motion of a cluster. This information canbe used to enhance or enable some of the classification techniquesdescribed below. Additionally, tracking clusters of points can beemployed to identify incorrect and thus potentially hazardoussituations. For example, a cluster that was not present in previousframes and is not close to a known border of the field of view mayindicate an error condition.

In some cases it may be sufficient to filter out clusters below acertain size and to identify cluster transitions that indicate errorstates. In other cases, however, it may be necessary to further classifyobjects into one or more of four categories: (1) elements of themachinery being controlled by system 112, (2) the workpiece orworkpieces that the machinery is operating on, and (3) other foreignobjects, including people, that may be moving in unpredictable ways andthat can be harmed by the machinery. It may or may not be necessary toconclusively classify people versus other unknown foreign objects. Itmay be necessary to definitively identify elements of the machinery assuch, because by definition these will always be in a state of“collision” with the machinery itself and thus will cause the system toerroneously stop the machinery if detected and not properly classified.Similarly, machinery typically comes into contact with workpieces, butit is typically hazardous for machinery to come into contact withpeople. Therefore, analysis module 342 should be able to distinguishbetween workpieces and unknown foreign objects, especially people.

Elements of the machinery itself may be handled for classificationpurposes by the optional background-subtraction calibration stepdescribed above. In cases where the machinery changes shape, elements ofthe machinery can be identified and classified, e.g., by supplyinganalysis module 342 with information about these elements (e.g., asscalable 3D representations), and in some cases (such as industrialrobot arms) providing a source of instantaneous information about thestate of the machinery. Analysis module 342 may be “trained” byoperating machinery, conveyors, etc. in isolation under observation bythe sensors 102, allowing analysis module 342 to learn their preciseregions of operation resulting from execution of the full repertoire ofmotions and poses. Analysis module 342 may classify the resultingspatial regions as occupied.

Conventional computer-vision techniques may be employed to enableanalysis module 342 to distinguish between workpieces and humans. Theseinclude deep learning, a branch of machine learning designed to usehigher levels of abstraction in data. The most successful of thesedeep-learning algorithms have been CNNs and, more recently, recurrentneural networks (RNNs). However, such techniques are generally employedin situations where accidental misidentification of a human as anon-human does not cause safety hazards. In order to use such techniquesin the present environment, a number of modifications may be needed.First, machine-learning algorithms can generally be tuned to preferfalse positives or false negatives (for example, logistic regression canbe tuned for high specificity and low sensitivity). False positives inthis scenario do not create a safety hazard—if the robot mistakes aworkpiece for a human, it will react conservatively. Additionally,multiple algorithms or neural networks based on different imageproperties can be used, promoting the diversity that may be key toachieving sufficient reliability for safety ratings. One particularlyvaluable source of diversity can be obtained by using sensors thatprovide both 3D and 2D image data of the same object. If any onetechnique identifies an object as human, the object will be treated ashuman. Using multiple techniques or machine-learning algorithms, alltuned to favor false positives over false negatives, sufficientreliability can be achieved. In addition, multiple images can be trackedover time, further enhancing reliability—and again every object can betreated as human until enough identifications have characterized it asnon-human to achieve reliability metrics. Essentially, this diversealgorithmic approach, rather than identifying humans, identifies thingsthat are definitely not humans.

In addition to combining classification techniques, it is possible toidentify workpieces in ways that do not rely on any type of humanclassification at all. One approach is to configure the system byproviding models of workpieces. For example, a “teaching” step in systemconfiguration may simply supply images or key features of a workpiece toanalysis module 342, which searches for matching configurations in spacemap 345, or may instead involve training of a neural network toautomatically classify workpieces as such in the space map. In eithercase, only objects that accurately match the stored model are treated asworkpieces, while all other objects are treated as humans.

Another suitable approach is to specify particular regions within theworkspace, as represented in the space map 345, where workpieces willenter (such as the top of a conveyor belt). Only objects that enter theworkspace in that location are eligible for treatment as workpieces. Theworkpieces can then be modeled and tracked from the time they enter theworkspace until the time they leave. While a monitored machine such as arobot is handling a workpiece, control system 112 ensures that theworkpiece is moving only in a manner consistent with the expected motionof the robot end effector. Known equipment such as conveyor belts canalso be modeled in this manner. Humans may be forbidden from enteringthe work cell in the manner of a workpiece—e.g., sitting on conveyors.

All of these techniques can be used separately or in combination,depending on design requirements and environmental constraints. In allcases, however, there may be situations where analysis module 342 losestrack of whether an identified object is a workpiece. In thesesituations the system should fall back to a safe state. An interlock canthen be placed in a safe area of the workspace where a human worker canconfirm that no foreign objects are present, allowing the system toresume operation.

In some situations a foreign object enters the workspace, butsubsequently should be ignored or treated as a workpiece. For example, astack of boxes that was not present in the workspace at configurationtime may subsequently be placed therein. This type of situation, whichwill become more common as flexible systems replace fixed guarding, maybe addressed by providing a user interface (e.g., shown in display 320or on a device in wireless communication with control system 112) thatallows a human worker to designate the new object as safe for futureinteraction. Of course, analysis module 342 and control routines 350 maystill act to prevent the machinery from colliding with the new object,but the new object will not be treated as a potentially human objectthat could move towards the machinery, thus allowing the system tohandle it in a less conservative manner.

The foregoing approach may be refined to permit a computer vision systemnot only to identify a workpiece but also judge its position andorientation to recognize proper positioning and alignment. Training isenhanced to make the analysis sensitive to these characteristics withinthe manufacturing environment. This can involve explicit 2D or 3Dcomputer-aided design (CAD) models of the workpieces in the properposition and orientation or training a neural network with many actualand/or synthetic images of such workpieces. Arrangements involvingmultiple workpieces may also be employed. The results of training aresaved in memory and recalled for future use. This process is describedin greater detail below.

3. Generating Control Outputs

At this stage, analysis module 342 has identified all objects in themonitored area 100 that must be considered. Given this data, a varietyof actions can be taken and control outputs generated. During staticcalibration or with the workspace in a default configuration free ofhumans, space map 345 may be useful to a human for evaluating sensorcoverage, the configuration of deployed machinery, and opportunities forunwanted interaction between humans and machines. Even without settingup cages or fixed guards, the overall workspace layout may be improvedby channeling or encouraging human movement through the regions markedas safe zones, as described above, and away from regions with poorsensor coverage.

Control routines 350, responsive to analysis module 342, may generatecontrol signals to operating machinery, such as robots, within workspace100 when certain conditions are detected. This control can be binary,indicating either safe or unsafe conditions, or can be more complex,such as an indication of what actions are safe and unsafe. The simplesttype of control signal is a binary signal indicating whether anintrusion of either occupied or potentially occupied volume is detectedin a particular zone. In the simplest case, there is a single intrusionzone and control system 112 provides a single output indicative of anintrusion. This output can be delivered, for example, via an I/O port327 to a complementary port on the controlled machinery to stop or limitthe operation of the machinery. In more complex scenarios, multiplezones are monitored separately, and a control routine 350 issues adigital output via an I/O port 327 or transceiver 325 addressed, over anetwork, to a target piece of machinery (e.g., using the Internetprotocol or other suitable addressing scheme).

Another condition that may be monitored is the distance between anyobject in the workspace and a machine, comparable to the output of a 2Dproximity sensor. This may be converted into a binary output byestablishing a proximity threshold below which the output should beasserted. It may also be desirable for the system to record and makeavailable the location and extent of the object closest to the machine.In other applications, such as a safety system for collaborativeindustrial robotics, the desired control output may include thelocation, shape, and extent of all objects observed within the areacovered by the sensors 102.

Another condition that may be monitored is the presence of workpieces ina location that the system has been trained to recognize. Workpieces maybe individually monitored or added to collections or groupings based onparticular process requirements. For example, some palletizingapplications pick one box at a time, warranting individual monitoring,while in other applications, boxes are picked three at a time, and acollection of three individually tracked objects would be moreappropriate. Variants of the same object may occupy the monitoredposition such as the end of a conveyor line. In other instances, logicalconditions related to the presence of multiple objects in differentlocations must be met prior to generating control outputs, e.g., anaction may be prevented until a certain number of workpieces havearrived at a designated location.

4. Safe Action Constraints and Dynamic Determination of Safe Zones

ISO 10218 and ISO/TS 15066 describe speed and separation monitoring as asafety function that can enable collaboration between an industrialrobot and a human worker. Risk reduction is achieved by maintaining atleast a protective separation distance between the human worker androbot during periods of robot motion. This protective separationdistance is calculated using information including robot and humanworker position and movement, robot stopping distance, measurementuncertainty, system latency and system control frequency. When thecalculated separation distance decreases to a value below the protectiveseparation distance, the robot system is stopped. This methodology canbe generalized beyond industrial robotics to machinery.

For convenience, the following discussion focuses on dynamicallydefining a safe zone around a robot operating in the workspace 100. Itshould be understood, however, that the techniques described hereinapply not only to multiple robots but to any form of machinery that canbe dangerous when approached too closely, and which has a minimum safeseparation distance that may vary over time and with particularactivities undertaken by the machine. As described above, a sensor arrayobtains sufficient image information to characterize, in 3D, the robotand the location and extent of all relevant objects in the areasurrounding the robot at each analysis cycle. (Each analysis cycleincludes image capture, refresh of the frame buffers, and computationalanalysis; accordingly, although the period of the analysis or controlcycle is short enough for effective monitoring to occur in real time, itinvolves many computer clock cycles.) Analysis module 342 utilizes thisinformation along with instantaneous information about the current stateof the robot at each cycle to determine instantaneous, current safeaction constraints for the robot's motion. The constraints may becommunicated to the robot, either directly by analysis module 342 or viaa control routine 350, to the robot via transceiver 325 and/or I/O port327.

5. Object Characterization and Tracking

The operation of the system is best understood with reference to theconceptual illustration of system organization and operation of FIG. 4A.As described above, a sensor array 102 monitors the workspace 400, whichincludes a robot 402. The robot's movements are controlled by aconventional robot controller 407, which may be part of or separate fromthe robot itself; for example, a single robot controller may issuecommands to more than one robot. The robot's activities may primarilyinvolve a robot arm, the movements of which are orchestrated by robotcontroller 407 using joint commands that operate the robot arm joints toeffect a desired movement. An object-monitoring system (OMS) 410 obtainsinformation about objects from the sensors 102 and uses this sensorinformation to identify relevant objects in the workspace 400. OMS 410communicates with robot controller 407 via any suitable wired orwireless protocol. (In an industrial robot, control electronicstypically reside in an external control box. However, in the case of arobot with a built-in controller, OMS 410 communicates directly with therobot's onboard controller.) Using information obtained from the robot(and, typically, sensors 102), OMS 410 determines the robot's currentstate. OMS 410 thereupon determines safe-action constraints for robot402 given the robot's current state and all identified relevant objects.Finally, OMS 410 communicates safe action constraints to robot 407. (Itwill be appreciated that, with reference to FIG. 3, the functions of OMS410 are performed in a control system 112 by analysis module 342 and, insome cases, a control routine 350.)

5.1 Identifying Relevant Objects

The sensors 102 provide real-time image information that is analyzed byan object-analysis module 415 at a fixed frequency in the mannerdiscussed above; in particular, at each cycle, object analysis module415 identifies the precise 3D location and extent of all objects inworkspace 400 that are either within the robot's reach or that couldmove into the robot's reach at conservative expected velocities. If notall of the relevant volume is within the collective field of view of thesensors 102, OMS 410 may be configured to so determine and indicate thelocation and extent of all fixed objects within that region (or aconservative superset of those objects) and/or verify that otherguarding techniques have been used to prevent access to unmonitoredareas.

5.2 Determining Workpiece Position and Orientation

Object analysis module 415 can be trained to recognize the desiredconfigurations of workpieces by physically positioning the workpieces inthe monitored zone where the sensors are actively monitoring the space.By subtracting a trained nominal background image from frames whereworkpieces are also present, object analysis module 415 performssegmentation and voxel-grid dissection to distinguish the properlypositioned configuration of workpieces from the background.

Beginning with a 3D voxel-grid volumetric representation of a space, aseries of image-capturing steps may be used to successfully train objectanalysis module 415 to recognize properly positioned and oriented 2Dworkpiece faces or 3D workpiece volumes. Object analysis module 415 mayfurther be trained to recognize a location of a properly orientedobject, e.g., relative to other objects and/or to surrounding featuressuch as a boundary. During training, the user sets workpieces in theirproper positions and orientations and signals approval; the imagerecorded by each sensor 102 is saved in memory as a comparison templateassociated with the specific type of workpiece (which is represented asan object). If multiple workpieces are processed (or their positions andorientations evaluated) simultaneously, the collection of objects may beapproved and stored. After being saved into memory, trained objects andcollections of objects may be recalled for additional editing andreclassification steps. Basic boolean operations to combine bodies maybe performed on the trained objects in addition to more complex additionand subtraction of individual voxels using a conventional CAD modelingtool. An offsetting tool may be used to adjust the boundary of theworkpiece by a configurable number of voxels. This boundary offsettingstep may be performed to account for multiple workpiece variants,part-to-part tolerance stack up or variance, or to influence thesensitivity of object analysis module 415 to the sensed object(s).Alternatively, conventional techniques of image data augmentation can beemployed to translate and rotate by small amounts the workpiece imagescaptured by the different sensors, thereby establishing the range ofacceptable variations in position and orientation.

The digital model may include representations of multiple discrete,sequential states; in the limit, these states may form a video sequenceof frames recorded as the workpiece moves through a production step. Forexample, a user interface may allow the user to pause the recording atany time and choose to record the current state of the workpiece intomemory as a discrete object. An alternative option in the continuousmode is to allow the object to change in real time based on the recordeddata.

The training process and subsequent operation are illustrated in FIGS.4B-4E for a palletizing application. In this sequential imaging process,visual understandings of the background and workpieces are developedseparately. With reference to FIG. 4B, the sensors 102 capture an imageof the static background including a conveyor 435. In FIG. 4C, a pallet437 is placed in position and the sensors 102 obtain new images of thetracked area. Using background subtraction from the first image, thevoxel representation of the pallet workpiece is identified and savedinto memory 310. Then box workpieces 440 are placed in the desired robotpick position at the end of conveyor 435 (FIG. 4D). Sensors 102 againacquire images, and again using background subtraction from the firsttwo images, the boxes 440 are identified as a collection of workpiecesand saved into memory 310. As a result, when new boxes are transportedon conveyor 435, they are not recognized as in proper position untilthree boxes are aligned and positioned as shown in FIG. 4D. Inoperation, as illustrated in FIG. 4E, when the predefined configurationof workpieces 400 is identified by object analysis module 415 (FIG. 4A),digital I/O is toggled based on configured logic, and robot controller407 operates robot 402 to pick up the collection of three boxes 440 andmove them to the placement position on pallet 437. Alternatively,information about workpiece configuration may be sent to robot 402 viaindustrial or real-time ethernet. Object analysis module 415 is able todistinguish between workpiece variants and signal to downstreamequipment which workpieces are present. Individual I/O points may bemapped to each trained configuration.

During training, a user interface may be configured to capture 3D imagesof the workspace or portion thereof and identify, or allow the user todesignate, volumes of interest in those images that representworkpieces. This may involve identifying workpieces by techniques suchas background subtraction or image-to-image subtraction to isolate thevoxels corresponding to the workpieces. For example, the user interfacemay present a series of user-facing tools enabling naming, grouping andmodification of workpiece spatial representations. Workpiecerepresentations may be imported to the system or exported by the systemto facilitate reusability of the workpieces in different applications.

In greater detail, object analysis module 415 may analyze the sensorimages and generate 3D point cloud data, which it may convert to avoxel-level representation of the workpieces 437, 440 followingbackground subtraction. When new images are received by sensors 102during operation, object analysis module 415 may convert these tovoxel-level representations for comparison to the stored models (whichalso may be represented as, or converted to, voxels) to assess whetherthe sensed workpieces conform in alignment and position to the storedmodels within an allowed tolerance. For example, the sensedrepresentation may be offset by a certain number of voxels to accountfor the desired level of misalignment tolerance. Alternatively, as notedabove, workpiece position and alignment may be analyzed by runningacquired sensor images through a neural network that has been trainedusing labeled images of properly aligned workpieces (with trainingimages including variations spanning the tolerance range) and misalignedworkpieces.

In a sequential or continuous mode where the workpiece representationtakes the form of a sequence of models or frames, the model may beplayed back as the workpiece is processed to ensure proper workpiecepositioning and alignment through the sequence rather than only beforeprocessing begins. For example, if filling a hopper with round objectsis recorded in a continuous training mode, it may be played back duringoperation upon the firing of digital input. A digital output on thehopper-filling system is fired when filling begins. That output isprovided to object analysis module 415, which begins playback of therecorded changing object state when the input is received andcontinuously compares input sensor images to the temporal model.

5.3 Determining Robot State

A robot state determination module (RSDM) 420 is responsive to data fromsensors 102 and signals from the robot 402 and/or robot controller 407to determine the instantaneous state of the robot. In particular, RSDM420 determines the pose and location of robot 402 within workspace 400;this may be achieved using sensors 102, signals from the robot and/orits controller, or data from some combination of these sources. RSDM 420may also determines the instantaneous velocity of robot 402 or anyappendage thereof; in addition, knowledge of the robot's instantaneousjoint accelerations or torques, or planned future trajectory may beneeded in order to determine motion constraints for the subsequent cycleas described below. Typically, this information comes from robotcontroller 407, but in some cases may be inferred directly from imagesrecorded by sensors 102 as described below.

For example, these data could be provided by the robot 402 or the robotcontroller 407 via a safety-rated communication protocol providingaccess to safety-rated data. The 3D pose of the robot may then bedetermined by combining provided joint positions with a static 3D modelof each link to obtain the 3D shape of the entire robot 402.

In some cases, the robot may provide an interface to obtain jointpositions that are not safety-rated, in which case the joint positionscan be verified against images from sensors 102 (using, for example,safety-rated software). For example, received joint positions may becombined with static 3D models of each link to generate a 3D model ofthe entire robot 402. This 3D image can be used to remove any objects inthe sensing data that are part of the robot itself. If the jointpositions are correct, this will fully eliminate all object dataattributed to the robot 402. If, however, the joint positions areincorrect, the true position of robot 402 will diverge from the model,and some parts of the detected robot will not be removed. Those pointswill then appear as a foreign object in the new cycle. In the previouscycle, it can be assumed that the joint positions were correct becauseotherwise robot 402 would have been halted. Since the base joint of therobot does not move, at least one of the divergent points must be closeto the robot. The detection of an unexpected object close to robot 402can then be used to trigger an error condition, which will cause controlsystem 112 (see FIG. 1) to transition robot 402 to a safe state.Alternately, sensor data can be used to identify the position of therobot using a correlation algorithm, such as described above in thesection on registration, and this detected position can be compared withthe joint position reported by the robot. If the joint positioninformation provided by robot 402 has been validated in this manner, itcan be used to validate joint velocity information, which can then beused to predict future joint positions. If these positions areinconsistent with previously validated actual joint positions, theprogram can similarly trigger an error condition. These techniquesenable use of a non-safety-rated interface to produce data that can thenbe used to perform additional safety functions.

Finally, RSDM 420 may be configured to determine the robot's joint stateusing only image information provided by sensors 102, without anyinformation provided by robot 402 or controller 407 sensors 102. Given amodel of all of the links in the robot, any of several conventional,well-known computer vision techniques can be used by RSDM 420 toregister the model to sensor data, thus determining the location of themodeled object in the image. For example, the ICP algorithm (discussedabove) minimizes the difference between two 3D point clouds. ICP oftenprovides a locally optimal solution efficiently, and thus can be usedaccurately if the approximate location is already known. This will bethe case if the algorithm is run every cycle, since robot 402 cannothave moved far from its previous position. Accordingly, globally optimalregistration techniques, which may not be efficient enough to run inreal time, are not required. Digital filters such as Kalman filters orparticle filters can then be used to determine instantaneous jointvelocities given the joint positions identified by the registrationalgorithm.

These image-based monitoring techniques often rely on being run at eachsystem cycle, and on the assumption that the system was in a safe stateat the previous cycle. Therefore, a test may be executed when robot 402is started—for example, confirming that the robot is in a known,pre-configured “home” position and that all joint velocities are zero.It is common for automated equipment to have a set of tests that areexecuted by an operator at a fixed interval, for example, when theequipment is started up or on shift changes. Reliable state analysistypically requires an accurate model of each robot link. This model canbe obtained a priori, e.g. from 3D CAD files provided by the robotmanufacturer or generated by industrial engineers for a specificproject. However, such models may not be available, at least not for therobot and all of the possible attachments it may have.

In this case, it is possible for RSDM 420 to create the model itself,e.g., using sensors 102. This may be done in a separate training modewhere robot 402 runs through a set of motions, e.g., the motions thatare intended for use in the given application and/or a set of motionsdesigned to provide sensors 102 with appropriate views of each link. Itis possible, but not necessary, to provide some basic information aboutthe robot a priori, such as the lengths and rotational axes of eachlink. During this training mode, RSDM 420 generates a 3D model of eachlink, complete with all necessary attachments. This model can then beused by RSDM 420 in conjunction with sensor images to determine therobot state.

5.4 Determining Safe-Action Constraints

In traditional axis- and rate-limitation applications, an industrialengineer calculates what actions are safe for a robot, given the plannedtrajectory of the robot and the layout of the workspace—forbidding someareas of the robot's range of motion altogether and limiting speed inother areas. These limits assume a fixed, static workplace environment.Here we are concerned with dynamic environments in which objects andpeople come, go, and change position; hence, safe actions are calculatedby a safe-action determination module (SADM) 425 in real time based onall sensed relevant objects and on the current state of robot 402, andthese safe actions may be updated each cycle. In order to be consideredsafe, actions should ensure that robot 402 does not collide with anystationary object, and also that robot 402 does not come into contactwith a person who may be moving toward the robot. Since robot 402 hassome maximum possible deceleration, controller 407 should be instructedto begin slowing the robot down sufficiently in advance to ensure thatit can reach a complete stop before contact is made.

One approach to achieving this is to modulate the robot's maximumvelocity (by which is meant the velocity of the robot itself or anyappendage thereof) proportionally to the minimum distance between anypoint on the robot and any point in the relevant set of sensed objectsto be avoided. The robot is allowed to operate at maximum speed when theclosest object is further away than some threshold distance beyond whichcollisions are not a concern, and the robot is halted altogether if anobject is within a certain minimum distance. Sufficient margin can beadded to the specified distances to account for movement of relevantobjects or humans toward the robot at some maximum realistic velocity.This is illustrated in FIG. 5. An outer envelope or 3D zone 502 isgenerated computationally by SADM 425 around the robot 504. Outside thiszone 502, all movements of the person P are considered safe because,within an operational cycle, they cannot bring the person sufficientlyclose to the robot 504 to pose a danger. Detection of any portion of theperson P's body within a second 3D zone 508, computationally definedwithin zone 502, is registered by SADM 425 but robot 504 is allowed tocontinue operating at full speed. If any portion of the person P crossesthe threshold of zone 508 but is still outside an interior danger zone510, robot 504 is signaled to operate at a slower speed. If any portionof the person P crosses into the danger zone 510—or is predicted to doso within the next cycle based on a model of human movement—operation ofrobot 504 is halted. These zones may be updated if robot 504 is moved(or moves) within the environment.

A refinement of this technique is for SADM 425 to control maximumvelocity proportionally to the square root of the minimum distance,which reflects the fact that in a constant-deceleration scenario,velocity changes proportionally to the square root of the distancetraveled, resulting in a smoother and more efficient, but still equallysafe, result. A further refinement is for SADM 425 to modulate maximumvelocity proportionally to the minimum possible time to collision—thatis, to project the robot's current state forward in time, project theintrusions toward the robot trajectory, and identify the nearestpotential collision. This refinement has the advantage that the robotwill move more quickly away from an obstacle than toward it, whichmaximizes throughput while still correctly preserving safety. Since therobot's future trajectory depends not just on its current velocity buton subsequent commands, SADM 425 may consider all points reachable byrobot 402 within a certain reaction time given its current jointpositions and velocities, and cause control signals to be issued basedon the minimum collision time among any of these states. Yet a furtherrefinement is for SADM 425 to take into account the entire plannedtrajectory of the robot when making this calculation, rather than simplythe instantaneous joint velocities. Additionally, SADM 425 may, viarobot controller 407, alter the robot's trajectory, rather than simplyalter the maximum speed along that trajectory. It is possible to choosefrom among a fixed set of trajectories one that reduces or eliminatespotential collisions, or even to generate a new trajectory on the fly.

While not necessarily a safety violation, collisions with staticelements of the workspace are generally not desirable. The set ofrelevant objects can include all objects in the workspace, includingboth static background such as walls and tables, and moving objects suchas workpieces and human workers. Either from prior configuration orrun-time detection, sensors 102 and analysis module 342 may be able toinfer which objects could possibly be moving. In this case, any of thealgorithms described above can be refined to leave additional margins toaccount for objects that might be moving, but to eliminate those marginsfor objects that are known to be static, so as not to reduce throughputunnecessarily but still automatically eliminate the possibility ofcollisions with static parts of the work cell.

Beyond simply leaving margins to account for the maximum velocity ofpotentially moving objects, state estimation techniques based oninformation detected by the sensing system can be used to project themovements of humans and other objects forward in time, thus expandingthe control options available to control routines 350. For example,skeletal tracking techniques can be used to identify moving limbs ofhumans that have been detected and limit potential collisions based onproperties of the human body and estimated movements of, e.g., aperson's arm rather than the entire person.

5.5 Communicating Safe Action Constraints to the Robot

The safe-action constraints identified by SADM 425 may be communicatedby OMS 410 to robot controller 407 on each cycle via a robotcommunication module 430. As described above, communication modules maycorrespond to an I/O port 327 interface to a complementary port on robotcontroller 407 or may correspond to transceiver 325. Most industrialrobots provide a variety of interfaces for use with external devices. Asuitable interface should operate with low latency at least at thecontrol frequency of the system. The interface can be configured toallow the robot to be programmed and run as usual, with a maximumvelocity being sent over the interface. Alternatively, some interfacesallow for trajectories to be delivered in the form of waypoints. Usingthis type of an interface, the intended trajectory of robot 402 can bereceived and stored within OMS 410, which may then generate waypointsthat are closer together or further apart depending on the safe-actionconstraints. Similarly, an interface that allows input of target jointtorques can be used to drive trajectories computed in accordanceherewith. These types of interface can also be used where SADM 425chooses new trajectories or modifies trajectories depending on thesafe-action constraints.

As with the interface used to determine robot state, if robot 402supports a safety-rated protocol that provides real-time access to therelevant safety-rated control inputs, this may be sufficient. However, asafety-rated protocol is not available, additional safety-rated softwareon the system can be used to ensure that the entire system remains safe.For example, SADM 425 may determine the expected speed and position ofthe robot if the robot is operating in accordance with the safe actionsthat have been communicated. SADM 425 then determines the robot's actualstate as described above. If the robot's actions do not correspond tothe expected actions, SADM 425 causes the robot to transition to a safestate, typically using an emergency stop signal. This effectivelyimplements a real-time safety-rated control scheme without requiring areal-time safety-rated interface beyond a safety-rated stoppingmechanism.

In some cases a hybrid system may be optimal—many robots have a digitalinput that can be used to hold a safety-monitored stop. It may bedesirable to use a communication protocol for variable speed, forexample, when intruding objects are relatively far from the robot, butto use a digital safety-monitored stop when the robot must come to acomplete stop, for example, when intruding objects are close to therobot.

Certain embodiments of the present invention are described above. It is,however, expressly noted that the present invention is not limited tothose embodiments; rather, additions and modifications to what isexpressly described herein are also included within the scope of theinvention.

What is claimed is:
 1. A system for identifying a workpiece in aprocessing environment, the system comprising: at least one sensor fordigitally recording visual information, at least one sensor beingpositioned to record at least one image of the workpiece at a locationwhere a specified position and orientation thereof is required; acomputer memory for storing a digital model of the workpiece; and aprocessor configured to determine, from the at least one recorded imageand the stored digital model, whether the workpiece conforms to thespecified position and orientation.
 2. The system of claim 1, whereinthe digital model includes a general 3D representation of the workpieceand a specific 3D representation of the workpiece in the specifiedposition and orientation.
 3. The system of claim 1, wherein theprocessor is configured to computationally generate, from at least onerecorded image, a 3D spatial representation of the workpiece.
 4. Thesystem of claim 1, wherein the processor is further configured togenerate a 3D voxel-grid volumetric representation of the location. 5.The system of claim 1, wherein the computer memory stores digital modelsof a plurality of workpieces, the processor being further configured torecognize a workpiece based on at least one recorded image andcomparison thereof to the stored digital models.
 6. The system of claim5, wherein each of the digital models include offset boundaries.
 7. Thesystem of claim 6, wherein the offset boundaries account for workpiecevariations.
 8. The system of claim 1, wherein the digital model is a CADrepresentation.
 9. The system of claim 1, wherein the digital model is amachine-learning representation.
 10. The system of claim 1, wherein thedigital model includes representations of multiple discrete, sequentialstates.
 11. A method for identifying a workpiece in a processingenvironment, the method comprising the steps of: digitally recording atleast one image of the workpiece where a specified position andorientation thereof is required; storing a digital model of theworkpiece; computationally determining, from the at least one recordedimage and the stored digital model, whether the workpiece conforms tothe specified position and orientation, and only if so, processing theworkpiece.
 12. The method of claim 11, wherein the digital modelincludes a general representation of the workpiece and a specificrepresentation of the workpiece in the specified position andorientation.
 13. The method of claim 11, further comprising the step ofcomputationally generating, from at least one recorded image, a 3Dspatial representation of the workpiece.
 14. The method of claim 11,further comprising the step of generating a 3D voxel-grid volumetricrepresentation of the location.
 15. The method of claim 11, whereindigital models of a plurality of workpieces are stored, and furthercomprising the step of computationally recognizing a workpiece based onat least one recorded image and comparison thereof to the stored digitalmodels.
 16. The method of claim 15, wherein each of the digital modelsincludes offset boundaries.
 17. The method of claim 16, wherein theoffset boundaries account for workpiece variations.
 18. The method ofclaim 12, wherein the digital model is a CAD representation.
 19. Themethod of claim 12, wherein the digital model is a machine-learningrepresentation.
 20. The method of claim 12, wherein the digital modelincludes representations of multiple discrete, sequential states. 21.The method of claim 12, wherein the digital model of the workpiece iscreated according to steps comprising: removing all non-workpieceobjects from the processing environment; digitally recording an image ofthe processing environment as at least one background image; physicallymoving a workpiece into the processing environment; digitally recordingan image of the workpiece in the specified position and orientation;subtracting the background image from the image of the workpiece in thespecified position and orientation to produce a difference image; andstoring the difference image as the digital model of the workpiece.